Toward personalizing treatment for depression: predicting diagnosis and severity.
Toward personalizing treatment for depression: predicting diagnosis and severity.
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DOI：
10.1136/amiajnl-2014-002733
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发表时间：
2014-11
期刊：
影响因子：
--
通讯作者：
Shah NH
中科院分区：
文献类型：
--
作者：
Huang SH;LePendu P;Iyer SV;Tai-Seale M;Carrell D;Shah NH
抑郁症是一种普遍存在的疾病，难以诊断和治疗。特别是，抑郁症患者对治疗的反应在很大程度上是不可预测的。为了实现抑郁症个性化治疗的目标，我们开发和评估使用电子健康记录 (EHR) 数据来预测抑郁症的诊断和严重程度以及对治疗的反应的计算模型。我们使用结构化诊断和药物代码以及自由文本临床报告，开发基于回归的模型，用于根据 EHR 数据预测抑郁症及其严重程度以及对治疗的反应。我们使用了两个数据集：来自帕洛阿尔托医学基金会的 35 000 名患者（5000 名抑郁症患者）和来自团体健康研究所的 5651 名接受抑郁症治疗的患者。我们的模型能够提前 12 个月预测未来的抑郁症诊断（受试者工作特征曲线下面积 (AUC) 0.70-0.80）。我们可以区分患有严重基线抑郁症的患者和患有轻微或轻度基线抑郁症的患者（AUC 0.72）。基线抑郁严重程度是药物和心理治疗反应的最强预测因素。使用 EHR 数据可以提前 12 个月预测抑郁症的诊断，并区分抑郁症的极端基线水平。这些模型使用有关诊断、药物和临床进展记录的常用数据，使其易于携带。自动确定严重程度的能力可以促进从多个地点组装具有相似严重程度的大型患者队列，这可能有助于阐明未来治疗反应的调节因素。
Depression is a prevalent disorder difficult to diagnose and treat. In particular, depressed patients exhibit largely unpredictable responses to treatment. Toward the goal of personalizing treatment for depression, we develop and evaluate computational models that use electronic health record (EHR) data for predicting the diagnosis and severity of depression, and response to treatment. We develop regression-based models for predicting depression, its severity, and response to treatment from EHR data, using structured diagnosis and medication codes as well as free-text clinical reports. We used two datasets: 35 000 patients (5000 depressed) from the Palo Alto Medical Foundation and 5651 patients treated for depression from the Group Health Research Institute. Our models are able to predict a future diagnosis of depression up to 12 months in advance (area under the receiver operating characteristic curve (AUC) 0.70–0.80). We can differentiate patients with severe baseline depression from those with minimal or mild baseline depression (AUC 0.72). Baseline depression severity was the strongest predictor of treatment response for medication and psychotherapy. It is possible to use EHR data to predict a diagnosis of depression up to 12 months in advance and to differentiate between extreme baseline levels of depression. The models use commonly available data on diagnosis, medication, and clinical progress notes, making them easily portable. The ability to automatically determine severity can facilitate assembly of large patient cohorts with similar severity from multiple sites, which may enable elucidation of the moderators of treatment response in the future.
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DOI：
10.1136/amiajnl-2011-000456
发表时间：
2012-03-01
影响因子：
6.4
作者：
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通讯作者：
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影响因子：
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影响因子：
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影响因子：
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影响因子：
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